Distributed Computing Perspectives on Theoretical Immunology

Meeting Summary:  This working group will convene computer scientists, mathematicians, experimental immunologists and experienced modelers to consider recent discoveries in experimental immunology, new datasets that have become available, and techniques in distributed and theoretical computing which may be applicable. The working group will consider topics such as distributed computing, compressive sensing, machine learning algorithms, and opportunities presented by the availability of whole-repertoire genomics and single-cell
imaging datasets.

SFI and its affiliated researchers pioneered the field of theoretical immunology, dating back at least to Perelson and Oster's seminal 1979 paper on shape spaces. Since then, a vibrant community of modelers has developed, who most often use differential equations and agentbased modeling methods to characterize immune phenomena. This approach has led to new approaches to treating HIV, the use of computational modeling in choosing vaccine strains, and informatics tools to predict protein allergenicity and cancer immunotherapy treatments. At the same time, computer scientists have used principles of immunology to develop computer algorithms for applications in computer security, anomaly detection in manufacturing, robotics and many more.